{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fdb9e5be",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 了解要求： 绘制未来15天的最高气温和最低气温变化图\n",
    "# 解决方法： 折线图绘制 ,pyplot 的plot()函数\n",
    "# plot(x, y, fmt , data=None, label=None, *args, **kwargs)\n",
    "\n",
    "# 日期数据,作为横轴\n",
    "# x = np.array([4,5,6,7,8,...])\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "\n",
    "#plt.rcParams['axes.unicode_minus'] = False ,#用来正常显示负号\n",
    "x = np.arange(4, 19)\n",
    "# 最高温\n",
    "y_max = np.array([32, 33, 34, 34, 33, 31, 30, 29, 30, 29, 26, 23, 21, 25, 31])\n",
    "# 最低温\n",
    "y_min = np.array([19, 19, 20, 22, 22, 21, 22, 16, 18, 18, 17, 14, 15, 16, 16])\n",
    "\n",
    "# 设置标签\n",
    "plt.xlabel(\"日期\")\n",
    "\n",
    "plt.ylabel(\"温度  ℃\")\n",
    "\n",
    "# 绘制最高温的图\n",
    "plt.plot(x, y_max)\n",
    "#绘制对低温的图\n",
    "plt.plot(x,y_min)\n",
    "\n",
    "\n",
    "# 显示\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "845d4801",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
